Search

Identifying genetic risk factors for highly heterogeneous disorders like epilepsy remains challenging. Here, we present the largest whole-exome sequencing study of epilepsy to date, with >54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets, and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies, generalized and focal epilepsies, while most other gene discoveries are subtype-specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single nucleotide/short indel-, copy number-, and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies., Competing Interests: Competing Interests B.M.N is a member of the scientific advisory board at Deep Genomics and Neumora. No other authors have competing interests to declare

Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, "concept" has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate semantic theories remain underspecified and/or opaque. Weak construct specificity is among the leading causes of the replication crisis now facing psychology and related fields. Term ambiguity hinders cross-disciplinary communication, falsifiability, and incremental theory-building. Numerous cognitive subdisciplines (e.g., vision, affective neuroscience) have recently addressed these limitations via the development of consensus-based guidelines and definitions. The project to follow represents our effort to produce a multidisciplinary semantic glossary consisting of succinct definitions, background, principled dissenting views, ratings of agreement, and subjective confidence for 17 target constructs (e.g., abstractness, abstraction, concreteness, concept, embodied cognition, event semantics, lexical-semantic, modality, representation, semantic control, semantic feature, simulation, semantic distance, semantic dimension). We discuss potential benefits and pitfalls (e.g., implicit bias, prescriptiveness) of these efforts to specify a common nomenclature that other researchers might index in specifying their own theoretical perspectives (e.g., They said X, but I mean Y)., (© 2024. The Author(s).)

Human language is expressive because it is compositional: The meaning of a sentence (semantics) can be inferred from its structure (syntax). It is commonly believed that language syntax and semantics are processed by distinct brain regions. Here, we revisit this claim using precision fMRI methods to capture separation or overlap of function in the brains of individual participants. Contrary to prior claims, we find distributed sensitivity to both syntax and semantics throughout a broad frontotemporal brain network. Our results join a growing body of evidence for an integrated network for language in the human brain within which internal specialization is primarily a matter of degree rather than kind, in contrast with influential proposals that advocate distinct specialization of different brain areas for different types of linguistic functions., (© 2024 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)

The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to analyze complex processes, providing flexible function approximation while preserving interpretability. To do so, we define and implement a nonlinear regression model in which the probability distribution over the response variable is parameterized by convolving the history of predictors over time using an artificial neural network, thereby allowing the shape and continuous temporal extent of effects to be inferred directly from time series data. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many cognitive processes and may critically affect the interpretation of data. We demonstrate substantial improvements on behavioral and neuroimaging data from the language processing domain, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions in cognitive (neuro)science that are otherwise hard to study., Competing Interests: Competing Interests: The authors declare no conflict of interests., (© 2024 Massachusetts Institute of Technology.)

Many studies of human language processing have shown that readers slow down at less frequent or less predictable words, but there is debate about whether frequency and predictability effects reflect separable cognitive phenomena: are cognitive operations that retrieve words from the mental lexicon based on sensory cues distinct from those that predict upcoming words based on context? Previous evidence for a frequency-predictability dissociation is mostly based on small samples (both for estimating predictability and frequency and for testing their effects on human behavior), artificial materials (e.g., isolated constructed sentences), and implausible modeling assumptions (discrete-time dynamics, linearity, additivity, constant variance, and invariance over time), which raises the question: do frequency and predictability dissociate in ordinary language comprehension, such as story reading? This study leverages recent progress in open data and computational modeling to address this question at scale. A large collection of naturalistic reading data (six datasets, >2.2 M datapoints) is analyzed using nonlinear continuous-time regression, and frequency and predictability are estimated using statistical language models trained on more data than is currently typical in psycholinguistics. Despite the use of naturalistic data, strong predictability estimates, and flexible regression models, results converge with earlier experimental studies in supporting dissociable and additive frequency and predictability effects., Competing Interests: Competing Interests: The author declares no conflict of interests., (© 2024 Massachusetts Institute of Technology.)

During real-time language comprehension, our minds rapidly decode complex meanings from sequences of words. The difficulty of doing so is known to be related to words' contextual predictability, but what cognitive processes do these predictability effects reflect? In one view, predictability effects reflect facilitation due to anticipatory processing of words that are predictable from context. This view predicts a linear effect of predictability on processing demand. In another view, predictability effects reflect the costs of probabilistic inference over sentence interpretations. This view predicts either a logarithmic or a superlogarithmic effect of predictability on processing demand, depending on whether it assumes pressures toward a uniform distribution of information over time. The empirical record is currently mixed. Here, we revisit this question at scale: We analyze six reading datasets, estimate next-word probabilities with diverse statistical language models, and model reading times using recent advances in nonlinear regression. Results support a logarithmic effect of word predictability on processing difficulty, which favors probabilistic inference as a key component of human language processing., Competing Interests: Competing interests statement:The authors declare no competing interest.

Language comprehension and the ability to infer others' thoughts (theory of mind [ToM]) are interrelated during development and language use. However, neural evidence that bears on the relationship between language and ToM mechanisms is mixed. Although robust dissociations have been reported in brain disorders, brain activations for contrasts that target language and ToM bear similarities, and some have reported overlap. We take another look at the language-ToM relationship by evaluating the response of the language network, as measured with fMRI, to verbal and nonverbal ToM across 151 participants. Individual-participant analyses reveal that all core language regions respond more strongly when participants read vignettes about false beliefs compared to the control vignettes. However, we show that these differences are largely due to linguistic confounds, and no such effects appear in a nonverbal ToM task. These results argue against cognitive and neural overlap between language processing and ToM. In exploratory analyses, we find responses to social processing in the "periphery" of the language network-right-hemisphere homotopes of core language areas and areas in bilateral angular gyri-but these responses are not selectively ToM-related and may reflect general visual semantic processing., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortex, where the language system resides, is characterized by high structural and functional inter-individual variability. This variability reduces the sensitivity and functional resolution of group-averaging analyses. This problem is compounded by the fact that language areas often lay in close proximity to regions of other large-scale networks with different functional profiles. A solution inspired by other fields of cognitive neuroscience (e.g., vision) is to identify language areas functionally in each individual brain using a 'localizer' task (e.g., a language comprehension task). This approach has proven productive in fMRI, yielding a number of discoveries about the language system, and has been successfully extended to intracranial recording investigations. Here, we apply this approach to MEG. Across two experiments (one in Dutch speakers, n=19; one in English speakers, n=23), we examined neural responses to the processing of sentences and a control condition (nonword sequences). We demonstrated that the neural response to language is spatially consistent at the individual level. The language-responsive sensors of interest were, as expected, less responsive to the nonwords condition. Clear inter-individual differences were present in the topography of the neural response to language, leading to greater sensitivity when the data were analyzed at the individual level compared to the group level. Thus, as in fMRI, functional localization yields benefits in MEG and thus opens the door to probing fine-grained distinctions in space and time in future MEG investigations of language processing.

To understand language, we must infer structured meanings from real-time auditory or visual signals. Researchers have long focused on word-by-word structure building in working memory as a mechanism that might enable this feat. However, some have argued that language processing does not typically involve rich word-by-word structure building, and/or that apparent working memory effects are underlyingly driven by surprisal (how predictable a word is in context). Consistent with this alternative, some recent behavioral studies of naturalistic language processing that control for surprisal have not shown clear working memory effects. In this fMRI study, we investigate a range of theory-driven predictors of word-by-word working memory demand during naturalistic language comprehension in humans of both sexes under rigorous surprisal controls. In addition, we address a related debate about whether the working memory mechanisms involved in language comprehension are language specialized or domain general. To do so, in each participant, we functionally localize (1) the language-selective network and (2) the "multiple-demand" network, which supports working memory across domains. Results show robust surprisal-independent effects of memory demand in the language network and no effect of memory demand in the multiple-demand network. Our findings thus support the view that language comprehension involves computationally demanding word-by-word structure building operations in working memory, in addition to any prediction-related mechanisms. Further, these memory operations appear to be primarily conducted by the same neural resources that store linguistic knowledge, with no evidence of involvement of brain regions known to support working memory across domains. SIGNIFICANCE STATEMENT This study uses fMRI to investigate signatures of working memory (WM) demand during naturalistic story listening, using a broad range of theoretically motivated estimates of WM demand. Results support a strong effect of WM demand in the brain that is distinct from effects of word predictability. Further, these WM demands register primarily in language-selective regions, rather than in "multiple-demand" regions that have previously been associated with WM in nonlinguistic domains. Our findings support a core role for WM in incremental language processing, using WM resources that are specialized for language., (Copyright © 2022 the authors.)

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building)-relevant across many cognitive domains-to specialized knowledge structures (a particular language's phonology, lexicon, and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language-selective network and the multiple demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence, making it a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension, and that past claims to the contrary are likely due to methodological artifacts. Although future studies may discover some aspects of language that require the MD network, evidence to date suggests that those will not be related to core linguistic processes like lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation between memory and computation in the mind and brain.

The influence of stimuli in psycholinguistic experiments diffuses across time because the human response to language is not instantaneous. The linear models typically used to analyze psycholinguistic data are unable to account for this phenomenon due to strong temporal independence assumptions, while existing deconvolutional methods for estimating diffuse temporal structure model time discretely and therefore cannot be directly applied to natural language stimuli where events (words) have variable duration. In light of evidence that continuous-time deconvolutional regression (CDR) can address these issues (Shain & Schuler, 2018), this article motivates the use of CDR for many experimental settings, exposits some of its mathematical properties, and empirically evaluates the influence of various experimental confounds (noise, multicollinearity, and impulse response misspecification), hyperparameter settings, and response types (behavioral and fMRI). Results show that CDR (1) yields highly consistent estimates across a variety of hyperparameter configurations, (2) faithfully recovers the data-generating model on synthetic data, even under adverse training conditions, and (3) outperforms widely-used statistical approaches when applied to naturalistic reading and fMRI data. In addition, procedures for testing scientific hypotheses using CDR are defined and demonstrated, and empirically-motivated best-practices for CDR modeling are proposed. Results support the use of CDR for analyzing psycholinguistic time series, especially in a naturalistic experimental paradigm., (Copyright © 2021 Elsevier B.V. All rights reserved.)

What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general "multiple demand" (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, functional magnetic resonance imaging data collected during naturalistic story listening are compared with theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant- or trial-level variability, the neuroimaging and behavioral datasets were collected in nonoverlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal "core language network," in both left-hemispheric areas and their right hemispheric homotopic areas. These results argue against strong involvement of domain-general executive circuits in language comprehension., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objective: We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology., Methods: We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing., Results: Overall, we identified pathogenic or likely pathogenic variants in 40% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25%)., Significance: Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies., (Wiley Periodicals, Inc. © 2020 International League Against Epilepsy.)

Acetylation of the lysine residues in histones and other DNA-binding proteins plays a major role in regulation of eukaryotic gene expression. This process is controlled by histone acetyltransferases (HATs/KATs) found in multiprotein complexes that are recruited to chromatin by the scaffolding subunit transformation/transcription domain-associated protein (TRRAP). TRRAP is evolutionarily conserved and is among the top five genes intolerant to missense variation. Through an international collaboration, 17 distinct de novo or apparently de novo variants were identified in TRRAP in 24 individuals. A strong genotype-phenotype correlation was observed with two distinct clinical spectra. The first is a complex, multi-systemic syndrome associated with various malformations of the brain, heart, kidneys, and genitourinary system and characterized by a wide range of intellectual functioning; a number of affected individuals have intellectual disability (ID) and markedly impaired basic life functions. Individuals with this phenotype had missense variants clustering around the c.3127G>A p.(Ala1043Thr) variant identified in five individuals. The second spectrum manifested with autism spectrum disorder (ASD) and/or ID and epilepsy. Facial dysmorphism was seen in both groups and included upslanted palpebral fissures, epicanthus, telecanthus, a wide nasal bridge and ridge, a broad and smooth philtrum, and a thin upper lip. RNA sequencing analysis of skin fibroblasts derived from affected individuals skin fibroblasts showed significant changes in the expression of several genes implicated in neuronal function and ion transport. Thus, we describe here the clinical spectrum associated with TRRAP pathogenic missense variants, and we suggest a genotype-phenotype correlation useful for clinical evaluation of the pathogenicity of the variants., (Copyright © 2019 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Objective: Somatic variants are a recognized cause of epilepsy-associated focal malformations of cortical development (MCD). We hypothesized that somatic variants may underlie a wider range of focal epilepsy, including nonlesional focal epilepsy (NLFE). Through genetic analysis of brain tissue, we evaluated the role of somatic variation in focal epilepsy with and without MCD., Methods: We identified somatic variants through high-depth exome and ultra-high-depth candidate gene sequencing of DNA from epilepsy surgery specimens and leukocytes from 18 individuals with NLFE and 38 with focal MCD., Results: We observed somatic variants in 5 cases in SLC35A2, a gene associated with glycosylation defects and rare X-linked epileptic encephalopathies. Nonsynonymous variants in SLC35A2 were detected in resected brain, and absent from leukocytes, in 3 of 18 individuals (17%) with NLFE, 1 female and 2 males, with variant allele frequencies (VAFs) in brain-derived DNA of 2 to 14%. Pathologic evaluation revealed focal cortical dysplasia type Ia (FCD1a) in 2 of the 3 NLFE cases. In the MCD cohort, nonsynonymous variants in SCL35A2 were detected in the brains of 2 males with intractable epilepsy, developmental delay, and magnetic resonance imaging suggesting FCD, with VAFs of 19 to 53%; Evidence for FCD was not observed in either brain tissue specimen., Interpretation: We report somatic variants in SLC35A2 as an explanation for a substantial fraction of NLFE, a largely unexplained condition, as well as focal MCD, previously shown to result from somatic mutation but until now only in PI3K-AKT-mTOR pathway genes. Collectively, our findings suggest a larger role than previously recognized for glycosylation defects in the intractable epilepsies. Ann Neurol 2018., (© 2018 American Neurological Association.)

Objective: To improve phenotype definition in genetic studies of epilepsy, we assessed the familial aggregation of focal seizure types and of specific seizure symptoms within the focal epilepsies in families from the Epilepsy Phenome/Genome Project., Methods: We studied 302 individuals with nonacquired focal epilepsy from 149 families. Familial aggregation was assessed by logistic regression analysis of relatives' traits (dependent variable) by probands' traits (independent variable), estimating the odds ratio for each symptom in a relative given presence vs absence of the symptom in the proband., Results: In families containing multiple individuals with nonacquired focal epilepsy, we found significant evidence for familial aggregation of ictal motor, autonomic, psychic, and aphasic symptoms. Within these categories, ictal whole body posturing, diaphoresis, dyspnea, fear/anxiety, and déjà vu/jamais vu showed significant familial aggregation. Focal seizure type aggregated as well, including complex partial, simple partial, and secondarily generalized tonic-clonic seizures., Conclusion: Our results provide insight into genotype-phenotype correlation in the nonacquired focal epilepsies and a framework for identifying subgroups of patients likely to share susceptibility genes., (© 2017 American Academy of Neurology.)

Objective: We sought to identify genetic causes of early onset epileptic encephalopathies with burst suppression (Ohtahara syndrome and early myoclonic encephalopathy) and evaluate genotype-phenotype correlations., Methods: We enrolled 33 patients with a referral diagnosis of Ohtahara syndrome or early myoclonic encephalopathy without malformations of cortical development. We performed detailed phenotypic assessment including seizure presentation, electroencephalography, and magnetic resonance imaging. We confirmed burst suppression in 28 of 33 patients. Research-based exome sequencing was performed for patients without a previously identified molecular diagnosis from clinical evaluation or a research-based epilepsy gene panel., Results: In 17 of 28 (61%) patients with confirmed early burst suppression, we identified variants predicted to be pathogenic in KCNQ2 (n = 10), STXBP1 (n = 2), SCN2A (n = 2), PNPO (n = 1), PIGA (n = 1), and SEPSECS (n = 1). In 3 of 5 (60%) patients without confirmed early burst suppression, we identified variants predicted to be pathogenic in STXBP1 (n = 2) and SCN2A (n = 1). The patient with the homozygous PNPO variant had a low cerebrospinal fluid pyridoxal-5-phosphate level. Otherwise, no early laboratory or clinical features distinguished the cases associated with pathogenic variants in specific genes from each other or from those with no prior genetic cause identified., Interpretation: We characterize the genetic landscape of epileptic encephalopathy with burst suppression, without brain malformations, and demonstrate feasibility of genetic diagnosis with clinically available testing in >60% of our cohort, with KCNQ2 implicated in one-third. This electroclinical syndrome is associated with pathogenic variation in SEPSECS. Ann Neurol 2017;81:419-429., (© 2017 American Neurological Association.)

Purpose: Periventricular nodular heterotopia (PVNH) is a malformation of cortical development due to impaired neuronal migration resulting in the formation of nodular masses of neurons and glial cells in close proximity to the ventricular walls. We report the clinical characteristics of the largest case series of FLNA-negative patients with seizures and bilateral periventricular heterotopia., Methods: Participants were recruited through the Epilepsy Phenome/Genome Project (EPGP), a multicenter collaborative effort to collect detailed phenotypic data and DNA on a large number of individuals with epilepsy, including a cohort with symptomatic epilepsy related to PVNH. Included subjects had epilepsy, and MRI confirmed bilateral PVNH. Magnetic resonance imaging studies were visually and quantitatively reviewed to investigate the topographic extent of PVNH, symmetry, and laterality., Key Findings: We analyzed data on 71 patients with bilateral PVNH. The incidence of febrile seizures was 16.6%. There was at least one other family member with epilepsy in 36.9% of this population. Developmental delay was present in 21.8%. Focal onset seizures were the most common type of seizure presentation (79.3%). High heterotopia burden was strongly associated with female gender and trigonal nodular localization. There was no evidence for differences in brain volume between PVNH subjects and controls. No relationship was observed between heterotopic volume and gender, developmental delay, location of PVNH, ventricular or cerebellar abnormalities, laterality of seizure onset, age at seizure onset, and duration of epilepsy., Significance: A direct correlation was observed between high heterotopia burden, female gender, and trigonal location in this large cohort of FLNA-negative bilateral PVNH patients with epilepsy. Quantitative MRI measurements indicated that this correlation is based on the diffuse nature of the heterotopic nodules rather than on the total volume of abnormal heterotopic tissue., (Copyright © 2015 Elsevier Inc. All rights reserved.)